Effects of pharmacist care on hospitalizations in heart failure across outpatient and inpatient settings: A systematic review and meta‐analysis
Bibliographic record
Abstract
Aims Heart failure (HF) is major cause of unplanned (re)hospitalizations, especially in high‐risk patients such as those recently discharged or those with worsening HF. Hospital‐affiliated or clinic‐based pharmacists, though underutilized, may help reduce this burden. This systematic review and meta‐analysis assessed their impact on all‐cause and HF hospitalizations. Methods A systematic literature search using PUBMED and EMBASE and conducted according to PRISMA guidelines identified randomized controlled trials published up to November 2024. Eligible studies evaluated the effects of pharmacy interventions on hospitalizations and mortality among patients with HF. Studies with community pharmacy‐ or home‐based interventions were excluded. Study quality was appraised using the Cochrane risk‐of‐bias tool. Random‐effects models were applied to derive odds ratios (OR), with heterogeneity assessed using the I 2 statistic and Cochrane's Q test. Results Eleven studies were included, involving 3576 patients and a variety of pharmacist interventions. Pharmacists significantly reduced the odds of all‐cause hospitalizations compared to usual care (3472 patients, 927 events; OR 0.67, 95% confidence interval [CI]: 0.49–0.92, P = 0.0119). For HF hospitalizations (3442 patients, 504 events), similar results were retrieved (OR 0.64, 95% CI: 0.48–0.87, P = 0.0038). Heterogeneity was moderate for both analyses. Sensitivity analyses supported the robustness of these two analyses. Subgroup analyses indicated greater effectiveness in outpatient settings and when extended interventions were provided. Conclusions Across inpatient and outpatient settings, pharmacist interventions in HF significantly reduced all‐cause as well as HF hospitalizations. Our findings highlight the importance of integrating pharmacists into multidisciplinary teams to improve HF management for in‐ and outpatients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.048 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".